Taylor’s Hypothesis states, “measuring the collective search intent of multiple human beings in a semantic search network in a semantically indexed database increases the probability of returning the desired search result. The value of the network is proportional to the square of the number of measured collective search intents in the system. The probability of achieving optimal results increases proportionally to the number of people participating in the network to achieve a desired result. As the number of search users increases regarding harvesting a similar search result, the probability of obtaining the desired result increases proportionally. The greater the number of participating users in the network, the more valuable the knowledge gained becomes to the community. The greater the quantity and variety of information in a semantically indexed database relative to the information sought the greater the probability that the information searched for may be located and harvested.”

The Yoogli semantic search algorithm is continuously and automatically re-mastered by tracing the semantic footprints of each user searching for a specific subject beginning at search number one using Wikipedia. The Taylor Rank algorithm tracks, records and refines each user’s search choices from the original search result from Wikipedia. The continuously refined Taylor Rank algorithm uses an automated re-mastering of the Yoogli semantic search algorithm based on a computer analysis of a minimum of 10,000 user semantic search footprints for any given subject/search. The beginning of the search path that all users take begins when they choose “more articles like this” from the first Yoogli keyword search result from Wikipedia as the initial search result delivered to a user.

The Taylor Rank algorithm is an automated computer program that continuously re-creates, re-masters and improves semantic search results for users. The algorithm is based on an analysis of each user’s historical search footprint on a specific subject combined with all other searches on the same subject, rather than an analysis of the quality and number of links to each page searched for as is the case with Google’s Page Rank algorithm. The Taylor Rank algorithm learns from itself, continuously updates itself, and provides more perfected and more pinpointed search results to the user. In other words, each Yoogli user may have a unique custom semantic search algorithm that understands and responds to each user’s specific search requests, based on both their own search footprint as well as the cumulative search footprints of thousands of other search users seeking the same information from their individual search queries.

The name of this automated semantic page results ranking algorithm system is Taylor Rank in contrast to Page Rank, which is named after Allan Page of Google for its famous keyword page ranking results algorithm. The TaylorRank technology takes the patented semantic search technology developed by Yoogli to the next level in providing more pinpointed search results for a user. It does this by adding and examining the behavioral paths that humans take using the Yoogli technology in harnessing prior semantic search results to achieve even more optimal search results against a specific subject of interest. This is the embodiment of “semantic network intelligence” which is a precursor of “semantic artificial intelligence.”